Get Data

This is nearly entirely based on the code in notebook 09 and that in 11.

We have latent variable expression analysis data - Latent Variable Feather File

For this data we are also using any data for which there are gene variants (cNFs, pNFs, MPNSTs): - Exome-Seq variants - WGS Variants

Let’s see if there are any LVs that split based on gene variant. Because we’re having trouble scaling with the number of latent variables, I only look at variants that occur in less than 5% of the population. notice this is a difference from notebook #11.

wgs.vars=synTableQuery("SELECT Hugo_Symbol,Protein_position,specimenID,IMPACT,FILTER,ExAC_AF FROM syn20551862")$asDataFrame()
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exome.vars=synTableQuery("SELECT Hugo_Symbol,Protein_position,specimenID,IMPACT,FILTER,ExAC_AF FROM syn20554939")$asDataFrame()
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all.vars<-rbind(select(wgs.vars,'Hugo_Symbol','Protein_position','specimenID','IMPACT','ExAC_AF'),
    select(exome.vars,'Hugo_Symbol','Protein_position','specimenID','IMPACT','ExAC_AF'))%>%
  subset(ExAC_AF<0.05)


fn <- tempfile(pattern = "", fileext = ".feather")
download.file('https://github.com/Sage-Bionetworks/nf-lv-viz/raw/master/data/filt_nf_mp_res.feather', destfile = fn)
mp_res<-read_feather(fn) %>% filter(sex != "NA", isCellLine != "TRUE")%>%
  select(latent_var,id,value,sd_value,specimenID,tumorType,modelOf,diagnosis)

Merge data together

For the purposes of this analysis we want to have only those samples wtih genomic data.

samps<-intersect(mp_res$specimenID,all.vars$specimenID)
print(samps)
##  [1] "2-019 Neurofibroma"                           
##  [2] "2-021 Neurofibroma"                           
##  [3] "2-001 Plexiform Neurofibroma"                 
##  [4] "2-012 Neurofibroma"                           
##  [5] "2-029 Neurofibroma"                           
##  [6] "2-025 Neurofibroma"                           
##  [7] "2-014 Neurofibroma"                           
##  [8] "2-032 Plexiform Neurofibroma"                 
##  [9] "2-004 Plexiform Neurofibroma"                 
## [10] "2-013 Plexiform Neurofibroma"                 
## [11] "2-031 Malignant Peripheral Nerve Sheath Tumor"
## [12] "2-010 Neurofibroma"                           
## [13] "2-026 Neurofibroma"                           
## [14] "2-016 Neurofibroma"                           
## [15] "2-005 Neurofibroma"                           
## [16] "2-017 Neurofibroma"                           
## [17] "2-006 Plexiform Neurofibroma"                 
## [18] "patient10tumor1"                              
## [19] "patient10tumor2"                              
## [20] "patient10tumor3"                              
## [21] "patient11tumor1"                              
## [22] "patient13tumor3"                              
## [23] "patient2tumor1"                               
## [24] "patient3tumor1"                               
## [25] "patient3tumor2"                               
## [26] "patient3tumor4"                               
## [27] "patient4tumor1"                               
## [28] "patient4tumor10"                              
## [29] "patient4tumor4"                               
## [30] "patient4tumor9"                               
## [31] "patient5tumor5"                               
## [32] "patient6tumor4"                               
## [33] "patient6tumor5"                               
## [34] "patient6tumor6"                               
## [35] "patient6tumor7"                               
## [36] "patient8tumor4"                               
## [37] "patient8tumor5"                               
## [38] "patient8tumor6"                               
## [39] "patient9tumor1"                               
## [40] "patient9tumor6"

Retrieve Variant Data

Let’s retrieve the LV data and evaluate any correlations between scores and tumor size or patient age

data.with.var<-mp_res%>%subset(specimenID%in%samps)%>%
  left_join(all.vars,by='specimenID')

tab<-subset(data.with.var,!tumorType%in%c('Other','High Grade Glioma','Low Grade Glioma'))

top.genes=tab%>%group_by(tumorType)%>%
  mutate(numSamps=n_distinct(specimenID))%>%
      group_by(tumorType,Hugo_Symbol)%>%
    mutate(numMutated=n_distinct(specimenID))%>%
    ungroup()%>%
  subset(numMutated>2)%>%
      subset(numMutated<(numSamps-1))%>%
  select(tumorType,Hugo_Symbol,numSamps,numMutated)%>%distinct()

gene.count=top.genes%>%group_by(tumorType)%>%mutate(numGenes=n_distinct(Hugo_Symbol))%>%select(tumorType,numGenes)%>%distinct()

DT::datatable(gene.count)

## Test significance of each gene/immune population

Now we can loop through every tumor type and gene

red.genes<-c("NF1","SUZ12","CDKN2A","EED")##for testing

vals<-tab%>%#subset(Hugo_Symbol%in%red.genes)%>%
    mutate(mutated=ifelse(is.na(IMPACT),'WT','Mutated'))%>%
  select(latent_var,tumorType,value,Hugo_Symbol,specimenID,mutated)%>%
  distinct()%>%
  spread(key=Hugo_Symbol,value='mutated',fill='WT')

counts<-vals%>%
  gather(key=gene,value=status,-c(latent_var,tumorType,value,specimenID))%>% 
    select(latent_var,tumorType,value,gene,specimenID,status)%>%
    group_by(latent_var,tumorType,gene)%>%
    mutate(numVals=n_distinct(status))%>%
    subset(numVals==2)%>%ungroup()

#so now we have only 
with.sig<-counts%>%ungroup()%>%subset(gene%in%top.genes$Hugo_Symbol)%>%
    group_by(latent_var,gene)%>%
  mutate(pval=t.test(value~status)$p.value)%>%ungroup()%>%
  group_by(latent_var)%>%
  mutate(corP=p.adjust(pval))%>%ungroup()%>%
  select(latent_var,gene,pval,corP)%>%distinct()

sig.vals<-subset(with.sig,corP<0.05)

DT::datatable(sig.vals)

Interesting! Some genes actually pass p-value correction. What do they look like? Here let’s write the messiest possible code to print.

for(ct in unique(sig.vals$latent_var)){
    tplot<-sig.vals[which(sig.vals$latent_var==ct),]
    if(nrow(tplot)==0)
      next
    print(tplot)
    p<-counts%>%
    subset(latent_var==ct)%>%
    subset(gene%in%tplot$gene)%>%
    ggplot(aes(x=gene,y=value,col=status))+
    geom_boxplot(outlier.shape=NA)+
    geom_point(position=position_jitterdodge(),aes(group=status))+
    theme(axis.text.x = element_text(angle = 90, hjust = 1))+
    ggtitle(paste(ct,'scores'))
#    if(method=='cibersort')
#      p<-p+scale_y_log10()
    print(p)
  }
## # A tibble: 104 x 4
##    latent_var                            gene           pval        corP
##    <chr>                                 <chr>         <dbl>       <dbl>
##  1 74,REACTOME_UNFOLDED_PROTEIN_RESPONSE AADACL4    1.12e-10 0.0000331  
##  2 74,REACTOME_UNFOLDED_PROTEIN_RESPONSE AC018445.1 1.12e-10 0.0000331  
##  3 74,REACTOME_UNFOLDED_PROTEIN_RESPONSE AL645922.1 1.12e-10 0.0000331  
##  4 74,REACTOME_UNFOLDED_PROTEIN_RESPONSE ANKRD34B   2.63e-12 0.000000781
##  5 74,REACTOME_UNFOLDED_PROTEIN_RESPONSE ANKRD65    9.04e-11 0.0000268  
##  6 74,REACTOME_UNFOLDED_PROTEIN_RESPONSE AP3S1      1.12e-10 0.0000331  
##  7 74,REACTOME_UNFOLDED_PROTEIN_RESPONSE APEX1      9.04e-11 0.0000268  
##  8 74,REACTOME_UNFOLDED_PROTEIN_RESPONSE AQP1       1.12e-10 0.0000331  
##  9 74,REACTOME_UNFOLDED_PROTEIN_RESPONSE ARAF       1.12e-10 0.0000331  
## 10 74,REACTOME_UNFOLDED_PROTEIN_RESPONSE ARFGEF2    1.12e-10 0.0000331  
## # … with 94 more rows

## # A tibble: 2 x 4
##   latent_var gene                pval     corP
##   <chr>      <chr>              <dbl>    <dbl>
## 1 LV 828     AL109659.1 0.00000000200 0.000593
## 2 LV 828     IGKV6D-21  0.000000153   0.0456

## # A tibble: 1 x 4
##   latent_var gene               pval   corP
##   <chr>      <chr>             <dbl>  <dbl>
## 1 LV 830     AL109659.1 0.0000000483 0.0144

## # A tibble: 16 x 4
##    latent_var gene                pval    corP
##    <chr>      <chr>              <dbl>   <dbl>
##  1 LV 984     AL109659.1 0.00000000532 0.00158
##  2 LV 984     BCL2L13    0.0000000621  0.0184 
##  3 LV 984     CPEB2      0.0000000262  0.00777
##  4 LV 984     ESRRA      0.0000000637  0.0189 
##  5 LV 984     GLRA1      0.0000000978  0.0290 
##  6 LV 984     HOXD8      0.0000000621  0.0184 
##  7 LV 984     LILRA4     0.0000000978  0.0290 
##  8 LV 984     MOCOS      0.0000000621  0.0184 
##  9 LV 984     NUDT19     0.0000000978  0.0290 
## 10 LV 984     OR4C5      0.00000000658 0.00195
## 11 LV 984     OR8B2      0.0000000621  0.0184 
## 12 LV 984     PDIA6      0.0000000978  0.0290 
## 13 LV 984     SIMC1      0.0000000621  0.0184 
## 14 LV 984     WDR92      0.0000000621  0.0184 
## 15 LV 984     ZDHHC13    0.0000000621  0.0184 
## 16 LV 984     ZNF395     0.000000110   0.0325

## # A tibble: 4 x 4
##   latent_var              gene           pval     corP
##   <chr>                   <chr>         <dbl>    <dbl>
## 1 24,PID_DELTANP63PATHWAY APC   0.00000000149 0.000444
## 2 24,PID_DELTANP63PATHWAY KRT18 0.000000168   0.0498  
## 3 24,PID_DELTANP63PATHWAY PLOD3 0.00000000415 0.00123 
## 4 24,PID_DELTANP63PATHWAY PRH2  0.00000000415 0.00123

## # A tibble: 8 x 4
##   latent_var                   gene               pval   corP
##   <chr>                        <chr>             <dbl>  <dbl>
## 1 728,SVM Mast cells activated ASB8       0.0000000924 0.0274
## 2 728,SVM Mast cells activated BAG5       0.0000000924 0.0274
## 3 728,SVM Mast cells activated C14orf166B 0.0000000924 0.0274
## 4 728,SVM Mast cells activated COL2A1     0.0000000924 0.0274
## 5 728,SVM Mast cells activated GAK        0.0000000885 0.0263
## 6 728,SVM Mast cells activated JUP        0.000000161  0.0478
## 7 728,SVM Mast cells activated PGPEP1L    0.0000000924 0.0274
## 8 728,SVM Mast cells activated SNX1       0.0000000621 0.0184

## # A tibble: 3 x 4
##   latent_var                      gene              pval    corP
##   <chr>                           <chr>            <dbl>   <dbl>
## 1 671,REACTOME_COLLAGEN_FORMATION C20orf196 0.0000000224 0.00665
## 2 671,REACTOME_COLLAGEN_FORMATION EID2      0.000000118  0.0350 
## 3 671,REACTOME_COLLAGEN_FORMATION FAM210A   0.0000000513 0.0152

## # A tibble: 8 x 4
##   latent_var                       gene             pval    corP
##   <chr>                            <chr>           <dbl>   <dbl>
## 1 161,REACTOME_TRNA_AMINOACYLATION CLCC1    0.0000000760 0.0226 
## 2 161,REACTOME_TRNA_AMINOACYLATION DGCR8    0.0000000658 0.0195 
## 3 161,REACTOME_TRNA_AMINOACYLATION OR2A7    0.0000000616 0.0183 
## 4 161,REACTOME_TRNA_AMINOACYLATION POU5F1   0.0000000582 0.0173 
## 5 161,REACTOME_TRNA_AMINOACYLATION PTPRS    0.0000000432 0.0128 
## 6 161,REACTOME_TRNA_AMINOACYLATION SEMA3D   0.000000134  0.0398 
## 7 161,REACTOME_TRNA_AMINOACYLATION TSNAXIP1 0.0000000197 0.00585
## 8 161,REACTOME_TRNA_AMINOACYLATION UBTD2    0.0000000866 0.0257

## # A tibble: 2 x 4
##   latent_var gene          pval    corP
##   <chr>      <chr>        <dbl>   <dbl>
## 1 LV 822     ESRRA 0.000000134  0.0397 
## 2 LV 822     OR4C5 0.0000000161 0.00477

## # A tibble: 4 x 4
##   latent_var               gene            pval    corP
##   <chr>                    <chr>          <dbl>   <dbl>
## 1 1,REACTOME_MRNA_SPLICING HLA-C  0.00000000708 0.00210
## 2 1,REACTOME_MRNA_SPLICING LILRA6 0.000000121   0.0359 
## 3 1,REACTOME_MRNA_SPLICING PLOD3  0.0000000642  0.0191 
## 4 1,REACTOME_MRNA_SPLICING PRH2   0.0000000642  0.0191

## # A tibble: 2 x 4
##   latent_var gene         pval       corP
##   <chr>      <chr>       <dbl>      <dbl>
## 1 LV 397     HLA-C    1.52e- 7 0.0452    
## 2 LV 397     IGHV7-81 2.98e-11 0.00000886

## # A tibble: 1 x 4
##   latent_var          gene             pval   corP
##   <chr>               <chr>           <dbl>  <dbl>
## 1 84,KEGG_SPLICEOSOME IGHV3-11 0.0000000435 0.0129

## # A tibble: 2 x 4
##   latent_var          gene         pval      corP
##   <chr>               <chr>       <dbl>     <dbl>
## 1 82,PID_RAC1_PATHWAY IGHV7-81 6.93e- 8 0.0206   
## 2 82,PID_RAC1_PATHWAY KRT18    1.79e-10 0.0000532

## # A tibble: 1 x 4
##   latent_var                         gene      pval      corP
##   <chr>                              <chr>    <dbl>     <dbl>
## 1 881,REACTOME_DNA_STRAND_ELONGATION IRX6  1.14e-10 0.0000339

## # A tibble: 1 x 4
##   latent_var                                        gene        pval   corP
##   <chr>                                             <chr>      <dbl>  <dbl>
## 1 310,REACTOME_FORMATION_OF_THE_TERNARY_COMPLEX_AN… LONP1    3.99e-8 0.0119

## # A tibble: 1 x 4
##   latent_var gene           pval   corP
##   <chr>      <chr>         <dbl>  <dbl>
## 1 LV 71      NBPF12 0.0000000453 0.0134

## # A tibble: 2 x 4
##   latent_var gene             pval    corP
##   <chr>      <chr>           <dbl>   <dbl>
## 1 LV 53      NPIPB11 0.00000000562 0.00167
## 2 LV 53      TAS2R46 0.000000100   0.0297

## # A tibble: 1 x 4
##   latent_var gene          pval   corP
##   <chr>      <chr>        <dbl>  <dbl>
## 1 LV 835     OR4C5 0.0000000884 0.0263

## # A tibble: 1 x 4
##   latent_var gene         pval   corP
##   <chr>      <chr>       <dbl>  <dbl>
## 1 LV 60      PER3  0.000000144 0.0428

## # A tibble: 2 x 4
##   latent_var                     gene           pval     corP
##   <chr>                          <chr>         <dbl>    <dbl>
## 1 39,SVM Dendritic cells resting PLOD3 0.00000000108 0.000321
## 2 39,SVM Dendritic cells resting PRH2  0.00000000108 0.000321

## # A tibble: 2 x 4
##   latent_var gene           pval     corP
##   <chr>      <chr>         <dbl>    <dbl>
## 1 LV 962     PLOD3 0.00000000101 0.000299
## 2 LV 962     PRH2  0.00000000101 0.000299

## # A tibble: 1 x 4
##   latent_var                              gene                 pval    corP
##   <chr>                                   <chr>               <dbl>   <dbl>
## 1 116,REACTOME_INTERFERON_ALPHA_BETA_SIG… RP11-1396O13…     2.45e-8 0.00727

## # A tibble: 1 x 4
##   latent_var gene                pval       corP
##   <chr>      <chr>              <dbl>      <dbl>
## 1 LV 509     RP11-1396O13.13 3.51e-12 0.00000104

## # A tibble: 1 x 4
##   latent_var gene                    pval   corP
##   <chr>      <chr>                  <dbl>  <dbl>
## 1 LV 904     RP11-1396O13.13 0.0000000374 0.0111

#}

Breaking down by tumor type

At first glance it seems that a lot of these are separating out cNFs (i.e. mast cell signaling) from other types. However, I’m getting the same error I get in notebook number 11, so am unsure about how to proceed.